The subject matter disclosed herein relates to a method, a system and a computer readable medium for automatic segmentation of a medical image. More particularly, the disclosed subject matter relates to image processing, and to systems and methods for medical imaging with image recognition and image registration capabilities.
Segmentation of anatomical structures in medical images is a fundamental task in a number of clinical processes in the field of oncology, radiology and in planning surgical interventions. Exemplary techniques for imaging include conventional X-ray plane film radiography, computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”), and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”). Segmentation is used to measure the size and shape of anatomical structures, to guide spatial normalization of anatomy between individuals and to plan medical interventions. The spectrum of available segmentation approaches is broad, ranging from manual outlining of structures in 2D cross-sections to more developed methods that use a so called ‘registration’ to find optimal correspondences between 3D images and a labeled probability map or atlas. There are also known semiautomatic approaches that combine the efficiency and repeatability of automatic segmentation with the human judgment that can only come from skilled expertise.
Despite the fact that a large number of fully automatic and semiautomatic segmentation methods have been disclosed, still manual delineation is generally used as the technique of choice for image segmentation. Reluctance to use the fully automatic approach is due to the concerns about its insufficient reliability in cases where the target anatomy may differ from the norm, as well as due to high computational demands of the approach based on image registration.
Manually tracing the outlines on a contiguous set of 2D slices, and then combining them, can be time consuming and labor intensive. Time and labor increase significantly both as the number of image slices increase, and as a number and size of an organ, tumor, etc. in an anatomical area of interest increases. Quality of the outlining and quality of a produced 3D object depend on a resolution and contrast of the 2D slices, and on knowledge and judgment of the clinician performing the reconstruction.
Using reliable automatic image segmentation could save time and labor, and could increase precision by eliminating subjectivity of the clinician.
Automated image segmentation of organs faces certain challenges. Some organs are located in a soft tissue environment wherein resolution against surrounding structures has poor contrast since neighboring organs have similar density values. Furthermore, shape and position of organs may change periodically. Characteristics of abdominal organs also change from patient to patient including for example, shape, size and location of the organ. Imaging parameters of CT machines vary as well.
Significant efforts have also been directed toward the development of templates for segmentation of the human organs. In model-based segmentation, transforming a medical image, which is a CT image or a series of CT images (often called as a CT study, CT volume or CT exam) to a common reference frame is a useful step before commencing segmentation. Transforming a medical image to a common reference frame is generally called registering the medical image. It is assumed that anatomic regions of different medical images are found in approximately a same voxel region. Thus, a probability map (also called as probability atlas or statistical atlas) may be generated on the basis of a large number of medical images, that represents a probability that a given voxel is a part of a particular organ. Given the probability map, a new medical image may be registered to a reference image to determine transformation function or transfer parameters. The reference image is usually selected from medical images of the average patients. The transformation function may then be applied to transform the probability map data to a coordinate system of the medical image to be segmented to help initialize a segmentation algorithm.
Segmentation is the process of assigning labels to individual voxels in the data set. Automatic segmentation thereby means automated recognition and labeling of human anatomical structures in 2D or 3D digital scans of the human body.
Region growing techniques have also been widely used in image processing and analysis and include a process of recursively packing connected pixels according to some criteria. The process starts from one (or more) pixel called a seed, and checks all pixels in its immediate neighborhood. Those pixels that satisfy some criteria, for example, their intensity value is larger than a pre-defined threshold, are collected or grouped together. The process then continues recursively from each collected pixel until no more pixels can be collected. Apart from the above region growing method, other similar techniques are also known for this purpose, for example the so called active contour method expanding an initial seed volume surrounded by an initial contour to the segmented volume of the organ. Throughout the present description and claims, all feasible methods suitable for growing or expanding a segmented area from a seed is called ‘growing method’.
A major limitation with existing growing methods is the need for human interaction to manually select the seed. There were some efforts to achieve automatic seed selection, but no practically applicable solution has been found so far. It is also a problem that no automated validation has been provided so far to achieve a safe final check of the automated segmentation.
U.S. Pat. No. 7,259,762 discloses a method for detecting and segmenting organs and structures in a medical image. Structural connections are analyzed, registration and region growing is used in this known organ segmentation. US 2007/0160277 A1 discloses a method for segmenting various brain structures on MRI images using an anatomical template and graph cut algorithms in an iterative approach. US 2010/0054525 A1 discloses a system and a method for automatic recognition and labeling of anatomical structures and vessels in medical imaging scans.
Automatic segmentations (either atlas based or not) is a long researched topic, numerous articles are published in this topic. Examples are the following: 3D segmentation of liver, kidneys and spleen from CT images, Gyorgy Bekes et al. (in Computer Assisted Radiation Therapy, Int J CARS (2007) 2 (Suppl 1):S45-47); Construction of an abdominal probabilistic atlas and its application in segmentation, Hyunjin Park et al. (in IEEE Transactions on Medical Imaging, Vol. 22, No. 4, April 2003, pp. 483-492); and User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, Paul A. Yushkevich et al. (in NeuroImage 31 (2006) 1116-1128).
These known methods do not solve the above problems of automatic segmentation.
Thus, there is a need for a solution allowing an improvement over existing methods and systems. There is a need for automatic segmentation method, computer program and system eliminating as much as possible the shortcomings of known techniques. There is a particular need for an automatic segmentation method allowing automated seed selection and final validation of the segmentation results. There is also a need for an automatic segmentation method that eliminates the concerns about insufficient reliability of automated segmentation.